## Load the packages
#install.packages('gapminder')
library(gapminder)
head(gapminder)
## # A tibble: 6 × 6
## country continent year lifeExp pop gdpPercap
## <fct> <fct> <int> <dbl> <int> <dbl>
## 1 Afghanistan Asia 1952 28.8 8425333 779.
## 2 Afghanistan Asia 1957 30.3 9240934 821.
## 3 Afghanistan Asia 1962 32.0 10267083 853.
## 4 Afghanistan Asia 1967 34.0 11537966 836.
## 5 Afghanistan Asia 1972 36.1 13079460 740.
## 6 Afghanistan Asia 1977 38.4 14880372 786.
# Base plot
plot(x = gapminder$gdpPercap, y = gapminder$lifeExp)
# The default graph is scatter plot. However, you can
# specify your choice of graph using an argument code
plot(x = gapminder$gdpPercap, y = gapminder$lifeExp, type = "line")
## Warning in plot.xy(xy, type, ...): plot type 'line' will be truncated to first
## character
# the code above will produce a line graph.
# You can also use a boxplot to plot your data.
boxplot(gapminder$lifeExp ~ gapminder$continent)
# The syntax '~' above is used for several things. In the
# code above it reads 'Life expentancy 'as determined' by
# Continent'
# It is important to also know that box plot work when one
# of the data type is a categorical data
# We can also make histogram, which is used to see the
# distribution of a data
hist(gapminder$lifeExp, col = "pink")
# Notice in the code above that there is an argument 'col'
# which helps to add color to the graph.
# GGPLOT is much cleaner and better to create graphs for
# publications
# install.packages('ggplot')
library(ggplot2)
ggplot(data = gapminder, mapping = aes(x = gdpPercap, y = lifeExp))
# The argument 'mapping' helps to create a template where
# our data will be plotted Notice that, the '$' is not used
# to call out the data in a dataset, this is because of the
# 'aes' which tell the computer to look for the vectors
# inside the data 'gapminder' that was written before it.
# The code above doesn't include any graph, it sets the
# stage for the graphs. 'gg' in 'ggplot' stands for
# 'grammar of graphics'. In order to continue to use the
# 'template' we add a '+' sign after the code. This tells
# the computer that everything that will come after should
# be on it.
ggplot(data = gapminder, mapping = aes(x = gdpPercap, y = lifeExp)) +
geom_point()
# geom_points() helps to add scatter plot geom_line helps
# to add line graphs
ggplot(data = gapminder, mapping = aes(x = gdpPercap, y = lifeExp)) +
geom_line()
# To determine how something has changed overtime
ggplot(data = gapminder, mapping = aes(x = year, y = lifeExp)) +
geom_line()
# Change overtime by country will include an additional
# argument
ggplot(data = gapminder, mapping = aes(x = year, y = lifeExp,
by = country)) + geom_line()
# You can add color by country, by using the function
# 'color='
ggplot(data = gapminder, mapping = aes(x = year, y = lifeExp,
by = country, color = continent)) + geom_line()
ggplot(data = gapminder, mapping = aes(x = gdpPercap, y = lifeExp)) +
geom_point()
# We can rescale our x-axis for. This is helpful if you
# want to see your points more clearly
ggplot(data = gapminder, mapping = aes(x = gdpPercap, y = lifeExp)) +
geom_point() + scale_x_log10()
# Now our data is more dispersed because we have enlarged
# the scale.
# We can also change the transparency which is known as the
# alpha value
ggplot(data = gapminder, mapping = aes(x = gdpPercap, y = lifeExp)) +
geom_point(alpha = 0.5) + scale_x_log10()
# When you run the code above, you will notice that the
# plot are more transparent and you can see more data
# clearly
# How TO ADD A SMOOTH FIT OR BEST FIT LINE ACROSS A GRAPH
# i.e a line that is closes to all the data
ggplot(data = gapminder, mapping = aes(x = gdpPercap, y = lifeExp)) +
geom_point(alpha = 0.5) + scale_x_log10() + geom_smooth(method = "lm")
## `geom_smooth()` using formula = 'y ~ x'
# In the code above, we added, a linear model line into the
# graph
# We can change the color and size of the line.
ggplot(data = gapminder, mapping = aes(x = gdpPercap, y = lifeExp)) +
geom_point(alpha = 0.5) + scale_x_log10() + geom_smooth(method = "lm",
color = "red", size = 3)
## Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
## ℹ Please use `linewidth` instead.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.
## `geom_smooth()` using formula = 'y ~ x'
# HOW TO CREATE A SUBSET FROM A DATA E.G GAPMINDER
# Let's create a new data set that includes all the
# information of America in gapmider
americas <- gapminder[gapminder$continent == "Americas", ]
# Important notice are the use of double equal to sign.
# Also, the comma at the end of 'Americas' was added to
# tell the computer that we need all the columns in the row
# 'Americas'.
americas
## # A tibble: 300 × 6
## country continent year lifeExp pop gdpPercap
## <fct> <fct> <int> <dbl> <int> <dbl>
## 1 Argentina Americas 1952 62.5 17876956 5911.
## 2 Argentina Americas 1957 64.4 19610538 6857.
## 3 Argentina Americas 1962 65.1 21283783 7133.
## 4 Argentina Americas 1967 65.6 22934225 8053.
## 5 Argentina Americas 1972 67.1 24779799 9443.
## 6 Argentina Americas 1977 68.5 26983828 10079.
## 7 Argentina Americas 1982 69.9 29341374 8998.
## 8 Argentina Americas 1987 70.8 31620918 9140.
## 9 Argentina Americas 1992 71.9 33958947 9308.
## 10 Argentina Americas 1997 73.3 36203463 10967.
## # ℹ 290 more rows
head(americas)
## # A tibble: 6 × 6
## country continent year lifeExp pop gdpPercap
## <fct> <fct> <int> <dbl> <int> <dbl>
## 1 Argentina Americas 1952 62.5 17876956 5911.
## 2 Argentina Americas 1957 64.4 19610538 6857.
## 3 Argentina Americas 1962 65.1 21283783 7133.
## 4 Argentina Americas 1967 65.6 22934225 8053.
## 5 Argentina Americas 1972 67.1 24779799 9443.
## 6 Argentina Americas 1977 68.5 26983828 10079.
# HOW TO MAKE SMALL MULTIPLES OF DATA
# Using the subset 'americas'
ggplot(data = americas, mapping = aes(x = year, y = lifeExp)) +
geom_line() + facet_wrap(~country)
# You can see that we have created a subset of life
# expectancy against year in countries in america. One
# thing we should note is that text of the years on x axis
# and the values on y axis are not that clear. We can
# remedy this using the function 'theme'
ggplot(data = americas, mapping = aes(x = year, y = lifeExp)) +
geom_line() + facet_wrap(~country) + theme(axis.text.x = element_text(angle = 45))
# Now the years are not overlapping with each other.
# Also, the X and Y axis can be renamed on the graph using
# 'xlab' and 'ylab' functions.
ggplot(data = americas, mapping = aes(x = year, y = lifeExp)) +
geom_line() + facet_wrap(~country) + theme(axis.text.x = element_text(angle = 45)) +
xlab("Year")
# Observe that the X-axis has been change from 'year' to
# 'Year'
ggplot(data = americas, mapping = aes(x = year, y = lifeExp)) +
geom_line() + facet_wrap(~country) + theme(axis.text.x = element_text(angle = 45)) +
xlab("Year") + ylab("LifeExpectancy")
# Also the y axis has been change from 'lifeExp' to
# 'LifeExpectancy'
# HOW TO SAVE A FIGURE
# You save a figure using the 'ggsave' function. However,
# before you do that you will have to make the figure into
# an object that will be saved in the environment. Here I
# will make it object 'p' in my environment
p <- ggplot(data = americas, mapping = aes(x = year, y = lifeExp)) +
geom_line() + facet_wrap(~country) + theme(axis.text.x = element_text(angle = 45)) +
xlab("Year") + ylab("LifeExpectancy")
# Now I can save it.
ggsave(filename = "./AmericasLifeExp.png", plot = p, width = 20,
height = 15, dpi = 300, units = "cm")
# Now, it is saved in your project directory. The ggsave
# function will help you meet the specific journal
# requirements for each publication.